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Generative AI technologies such as large language models show novel potentials to enhance educational research.
W. H. Kruskal and W. A. Wallis, Use of ranks in one-criterion variance analysis, Journal of the American Statistical Association 47
1952
Earlier work this paper cites.
J. Cohen, Statistical power analysis for the behavioral sciences (Erlbaum, Hillsdale, NJ, 1988)
1988
Earlier work this paper cites.
D. Hestenes, M. Wells, and G. Swackhamer, Force concept inventory, The physics teacher 30
1992
Earlier work this paper cites.
A. diSessa, Toward an epistemology of physics, Cognition and Instruction 10
1993
Earlier work this paper cites.
R. R. Hake, Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses, American Journal of Physics 66
1998
Earlier work this paper cites.
D. Hammer, Student resources for learning introductory physics, American Journal of Physics 68
2000
Earlier work this paper cites.
D. A. Van Dyk and X.-L. Meng, The art of data augmentation, Journal of Computational and Graphical Statistics 10
2001
Earlier work this paper cites.
Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, A neural probabilistic language model, Journal of Machine Learning Research 3
2003
Earlier work this paper cites.
I. T. Koponen and M. Pehkonen, Coherent knowledge structures of physics represented as concept networks in teacher education, Science & Education 19
2010
Earlier work this paper cites.
X. Liu, Using and Developing Measurement Instruments in Science Education: A Rasch Modeling Approach , Science & Engineering Education Sources (Information Age Publishing Incorporated, Charlotte, NC, 2010)
2010
Earlier work this paper cites.
W. K. Adams and C. E. Wieman, Development and validation of instruments to measure learning of expert–like thinking, International Journal of Science Education 33
2011
Earlier work this paper cites.
R. H. Nehm and H. Härtig, Human vs. computer diagnosis of students’ natural selection knowledge: Testing the efficacy of text analytic software, Journal of Science Education and Technology 21
2012
Earlier work this paper cites.
L. Porter, C. Taylor, and K. C. Webb, Leveraging open source principles for flexible concept inventory development, in Proceedings of the 2014 conference on Innovation & technology in computer science education , ACM Digital Library, edited by Å. Cajander, M. Daniels, T. Clear, and A. Pears (ACM, New York, NY, 2014) pp. 243–248
2014
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, Attention is all you need: Conference on neural information processing systems, Advances in Neural Information Processing Systems , 6000 (2017)
2017
Earlier work this paper cites.
A. Caliskan, J. J. Bryson, and A. Narayanan, Semantics derived automatically from language corpora contain human-like biases, Science (New York, N.Y.) 356
2017
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv 1810.04805
2018
Earlier work this paper cites.
C. Shorten and T. M. Khoshgoftaar, A survey on image data augmentation for deep learning, Journal of big data 6
2019
Earlier work this paper cites.
J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, Scaling laws for neural language models, arXiv (2020)
2020
Cited alongside, same era.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, Language models are few-shot learners, arXiv (2020)
2020
Cited alongside, same era.
S. Küchemann, P. Klein, H. Fouckhardt, S. Gröber, and J. Kuhn, Students’ understanding of non-inertial frames of reference, Physical Review Physics Education Research 16
2020
Cited alongside, same era.
C. Ruggieri, Students’ use and perception of textbooks and online resources in introductory physics, Phys. Rev. Phys. Educ. Res. 16
2020
N. M. S. Surameery and M. Y. Shakor, Use chat gpt to solve programming bugs, International Journal of Information Technology & Computer Engineering (IJITC) ISSN: 2455-5290 3
2023
Closest in time.
R. K. Sinha, A. Deb Roy, N. Kumar, and H. Mondal, Applicability of chatgpt in assisting to solve higher order problems in pathology, Cureus 15
2023
Closest in time.
P. Wulff, Network analysis of terms in the natural sciences insights from wikipedia through natural language processing and network analysis, Education and Information Technologies 10.1007/s10639-022-11531-5 (2023)
2023
Closest in time.
T. Schubatzky, R. Wackermann, C. Wöhlke, C. Haagen-Schützenhöfer, M. Jedamski, H. Lindemann, and K. Cardinal, Entwicklung des concept-inventory ccci-422 zu den naturwissenschaftlichen grundlagen des klimawandels, Zeitschrift für Didaktik der Naturwissenschaften 29
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
R. Van De Schoot, J. De Bruin, R. Schram, P. Zahedi, J. De Boer, F. Weijdema, B. Kramer, M. Huijts, M. Hoogerwerf, G. Ferdinands, et al. , An open source machine learning framework for efficient and transparent systematic reviews, Nature machine intelligence 3
2021
Cited alongside, same era.
P. Eaton, Evidence of measurement invariance across gender for the force concept inventory, Phys Rev Spec Top Phys Edu Res (2021)
2021
Cited alongside, same era.
E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell, On the dangers of stochastic parrots, FAccT , 610 (2021)
2021
Cited alongside, same era.
B. K. Iwana and S. Uchida, An empirical survey of data augmentation for time series classification with neural networks, Plos one 16
2021
Cited alongside, same era.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing, arXiv (2021)
2021
Cited alongside, same era.
C. D. Manning, Human language understanding & reasoning, Daedalus 151
2022
Cited alongside, same era.
R. Rosenbaum, On the relationship between predictive coding and backpropagation, PloS one 17
2022
Cited alongside, same era.
A. Lewkowycz, A. Andreassen, D. Dohan, E. Dyer, H. Michalewski, V. Ramasesh, A. Slone, C. Anil, I. Schlag, T. Gutman-Solo, Y. Wu, B. Neyshabur, G. Gur-Ari, and V. Misra, Solving quantitative reasoning problems with language models, arXiv (2022)
2022
Cited alongside, same era.
S. Wolfram, What is ChatGPT doing and why does it work? (Wolfram Media, 2023)
2023
Closest in time.
Percentages are taken from https://en.wikipedia.org/wiki/GPT-3#GPT-3.5 (last access 26 May 2023) for training GPT 3
2023
Closest in time.
J. Huang and K. C.-C. Chang, Towards reasoning in large language models: A survey, arXiv (2023)
2023
Closest in time.
S. R. Bowman, Eight things to know about large language models, arXiv (2023)
2023
Closest in time.
D. M. Katz, M. J. Bommarito, S. Gao, and P. D. Arredondo, Gpt-4 passes the bar exam, SSRN 10.2139/ssrn.4389233 (2023)
2023
Closest in time.
G. Kortemeyer, Could an artificial-intelligence agent pass an introductory physics course?, Physical Review Physics Education Research 19
2023
Closest in time.
Chat can be accessed here: https://chat.openai.com/share/8ae109d2-8b71-4033-8c62-749c33c2582c , last access: June 2023; translated with: https://www.deepl.com/de/translator##de/en/ )
2023
Closest in time.
B. Gregorcic and A.-M. Pendrill, Chatgpt and the frustrated socrates, Physics Education 58
2023
Closest in time.
W. Yeadon, O.-O. Inyang, A. Mizouri, A. Peach, and C. P. Testrow, The death of the short-form physics essay in the coming ai revolution, Physics Education 58
2023
Closest in time.
J. Wang, Chatgpt: A test drive, American Journal of Physics 91
2023
Closest in time.
L. Krupp, S. Steinert, M. Kiefer-Emmanouilidis, K. Avila, P. Lukowicz, J. Kuhn, S. Kuechemann, and J. Karolus, Unreflected acceptance – investigating the negative consequences of chatgpt-assisted problem solving in physics education (2023)
2023
Closest in time.
S. Küchemann, S. Steinert, N. Revenga, M. Schweinberger, Y. Dinc, K. E. Avila, and J. Kuhn, Can chatgpt support prospective teachers in physics task development?, Phys. Rev. Phys. Educ. Res. 19
2023
Closest in time.